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Licensed Unlicensed Requires Authentication Published by De Gruyter November 19, 2020

Computational study for suppression of CD25/IL-2 interaction

  • Moein Dehbashi ORCID logo , Zohreh Hojati ORCID logo EMAIL logo , Majid Motovali-bashi , Mazdak Ganjalikhani-Hakemi , Akihiro Shimosaka and William C. Cho EMAIL logo
From the journal Biological Chemistry

Abstract

Cancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.


Corresponding authors: Zohreh Hojati, Division of Genetics, Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441, Islamic Republic of Iran, E-mail: ; and William C. Cho, Department of Clinical Oncology, Queen Elizabeth Hospital, HKSAR, China, E-mail:

Award Identifier / Grant number: 8489

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This study was financially supported by The Graduate Office of University of Isfahan (Grant No. 8489).

  3. Conflict of interest statement: The authors confirm that they have no conflicts of interest regarding this article.

References

Al-Qahtani, D., Anil, S., and Rajendran, R. (2011). Tumour infiltrating CD25+ FoxP3+ regulatory T cells (Tregs) relate to tumour grade and stromal inflammation in oral squamous cell carcinoma. J. Oral Pathol. Med. 40: 636–642. https://doi.org/10.1111/j.1600-0714.2011.01020.x.Search in Google Scholar PubMed

Amor, K.T., Ryan, C., and Menter, A. (2010). The use of cyclosporine in dermatology: part I. J. Am. Acad. Dermatol. 63: 925–946. https://doi.org/10.1016/j.jaad.2010.02.063.Search in Google Scholar PubMed

Arkin, M.R., Randal, M., DeLano, W.L., Hyde, J., Luong, T.N., Oslob, J.D., Raphael, D.R., Taylor, L., Wang, J., McDowell, R.S., et al.. (2003). Binding of small molecules to an adaptive protein-protein interface. Proc. Natl. Acad. Sci. U.S.A. 100: 1603–1608. https://doi.org/10.1073/pnas.252756299.Search in Google Scholar PubMed PubMed Central

Bates, G.J., Fox, S.B., Han, C., Leek, R.D., Garcia, J.F., Harris, A.L., and Banham, A.H. (2006). Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. J. Clin. Oncol. 24: 5373–5380. https://doi.org/10.1200/jco.2006.05.9584.Search in Google Scholar PubMed

Blaszczyk, M., Kurcinski, M., Kouza, M., Wieteska, L., Debinski, A., Kolinski, A., and Kmiecik, S. (2016). Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 93: 72–83. https://doi.org/10.1016/j.ymeth.2015.07.004.Search in Google Scholar PubMed

Chaudhary, B. and Elkord, E. (2016). Regulatory T cells in the tumor microenvironment and cancer progression: role and therapeutic targeting. Vaccines 4: 28. https://doi.org/10.3390/vaccines4030028.Search in Google Scholar PubMed PubMed Central

Chernikov, I.V., Vlassov, V.V., and Chernolovskaya, E.L. (2019). Current development of siRNA bioconjugates: from research to the clinic. Front. Pharmacol. 10: 444. https://doi.org/10.3389/fphar.2019.00444.Search in Google Scholar PubMed PubMed Central

Ciemny, M.P., Debinski, A., Paczkowska, M., Kolinski, A., Kurcinski, M., and Kmiecik, S. (2016). Protein-peptide molecular docking with large-scale conformational changes: the p53-MDM2 interaction. Sci. Rep. 6: 37532. https://doi.org/10.1038/srep37532.Search in Google Scholar PubMed PubMed Central

Ciemny, M.P., Kurcinski, M., Kozak, K.J., Kolinski, A., and Kmiecik, S. (2017). Highly flexible protein–peptide docking using CABS-dock. In: Modeling peptide–protein interactions. New York, NY: Humana Press, pp. 69–94.10.1007/978-1-4939-6798-8_6Search in Google Scholar PubMed

Coles, S.J., Hills, R.K., Wang, E.C.Y., Burnett, A.K., Man, S., Darley, R.L., and Tonks, A. (2012). Increased CD200 expression in acute myeloid leukemia is linked with an increased frequency of FoxP3+ regulatory T cells. Leukemia 26: 2146–2148. https://doi.org/10.1038/leu.2012.75.Search in Google Scholar PubMed PubMed Central

Curiel, T.J., Coukos, G., Zou, L., Alvarez, X., Cheng, P., Mottram, P., Evdemon-Hogan, M., Conejo-Garcia, J.R., Zhang, L., Burow, M., et al.. (2004). Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat. Med. 10: 942–949. https://doi.org/10.1038/nm1093.Search in Google Scholar PubMed

Curti, A., Trabanelli, S., Onofri, C., Aluigi, M., Salvestrini, V., Ocadlikova, D., Evangelisti, C., Rutella, S., De Cristofaro, R., Ottaviani, E., et al. (2010). Indoleamine 2, 3-dioxygenase-expressing leukemic dendritic cells impair a leukemia-specific immune response by inducing potent T regulatory cells. Haematologica 95: 2022–2030. https://doi.org/10.3324/haematol.2010.025924.Search in Google Scholar PubMed PubMed Central

de Vries, S.J., Rey, J., Schindler, C.E., Zacharias, M., and Tuffery, P. (2017). The pepATTRACT web server for blind, large-scale peptide-protein docking. Nucl. Acids Res. 45: W361–W364. https://doi.org/10.1093/nar/gkx335.Search in Google Scholar PubMed PubMed Central

deLeeuw, R.J., Kost, S.E., Kakal, J.A., and Nelson, B.H. (2012). The prognostic value of FoxP3+ tumor-infiltrating lymphocytes in cancer: a critical review of the literature. Clin. Canc. Res. 18: 3022–3029. https://doi.org/10.1158/1078-0432.ccr-11-3216.Search in Google Scholar PubMed

Duell, J., Dittrich, M., Bedke, T., Mueller, T., Eisele, F., Rosenwald, A., Rasche, L., Hartmann, E., Dandekar, T., Einsele, H., et al. (2017). Frequency of regulatory T cells determines the outcome of the T-cell-engaging antibody blinatumomab in patients with B-precursor ALL. Leukemia 31: 2181–2190. https://doi.org/10.1038/leu.2017.41.Search in Google Scholar PubMed PubMed Central

Dundas, J., Ouyang, Z., Tseng, J., Binkowski, A., Turpaz, Y., and Liang, J. (2006). CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucl. Acids Res. 34: W116–W118. https://doi.org/10.1093/nar/gkl282.Search in Google Scholar PubMed PubMed Central

Ekins, S., Mestres, J., and Testa, B. (2007). In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152: 9–20. https://doi.org/10.1038/sj.bjp.0707305.Search in Google Scholar PubMed PubMed Central

Gao, Q., Qiu, S.J., Fan, J., Zhou, J., Wang, X.Y., Xiao, Y.S., Xu, Y., Li, Y.W., and Tang, Z.Y. (2007). Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection. J. Clin. Oncol. 25: 2586–2593. https://doi.org/10.1200/jco.2006.09.4565.Search in Google Scholar

Garcia-Garcia, J., Guney, E., Aragues, R., Planas-Iglesias, J., and Oliva, B. (2010). Biana: a software framework for compiling biological interactions and analyzing networks. BMC Bioinf. 11: 56. https://doi.org/10.1186/1471-2105-11-56.Search in Google Scholar PubMed PubMed Central

Garcia-Garcia, J., Valls-Comamala, V., Guney, E., Andreu, D., Muñoz, F.J., Fernandez-Fuentes, N., and Oliva, B. (2017). iFrag: a protein-protein interface prediction server based on sequence fragments. J. Mol. Biol. 429: 382–389. https://doi.org/10.1016/j.jmb.2016.11.034.Search in Google Scholar PubMed

Ge, W., Ma, X., Li, X., Wang, Y., Li, C., Meng, H., Liu, X., Yu, Z., You, S., and Qiu, L. (2009). B7-H1 up-regulation on dendritic-like leukemia cells suppresses T cell immune function through modulation of IL-10/IL-12 production and generation of Treg cells. Leuk. Res. 33: 948–957. https://doi.org/10.1016/j.leukres.2009.01.007.Search in Google Scholar PubMed

Goodman, W.A., Cooper, K.D., and McCormick, T.S. (2012). Regulation generation: the suppressive functions of human regulatory T cells. Crit. Rev. Immunol. 32, https://doi.org/10.1615/critrevimmunol.v32.i1.40.Search in Google Scholar PubMed PubMed Central

Hakemi, M.G., Ghaedi, K., Andalib, A., Homayouni, V., Hosseini, M., and Rezaei, A. (2013). RORC2 gene silencing in human Th17 cells by siRNA: design and evaluation of highly efficient siRNA. Avicenna J. Med. Biotechnol. (AJMB) 5: 10.Search in Google Scholar

Jackson, A.L., Bartz, S.R., Schelter, J., Kobayashi, S.V., Burchard, J., Mao, M., Li, B., Cavet, G., and Linsley, P.S. (2003). Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 21: 635–637. https://doi.org/10.1038/nbt831.Search in Google Scholar PubMed

Jiang, Y., Du, Z., Yang, F., Di, Y., Li, J., Zhou, Z., Pillarisetty, V.G., and Fu, D. (2014). FOXP3+ lymphocyte density in pancreatic cancer correlates with lymph node metastasis. PLoS One 9: e106741. https://doi.org/10.1371/journal.pone.0106741.Search in Google Scholar PubMed PubMed Central

Jiménez-García, B., Pons, C., and Fernández-Recio, J. (2013). pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring. Bioinformatics 29: 1698–1699. https://doi.org/10.1093/bioinformatics/btt262.Search in Google Scholar PubMed

Kawabata, T. and Go, N. (2007). Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites. Proteins 68: 516–529. https://doi.org/10.1002/prot.21283.Search in Google Scholar PubMed

Kindlund, B., Sjöling, Å., Yakkala, C., Adamsson, J., Janzon, A., Hansson, L.E., Hermansson, M., Janson, P., Winqvist, O., and Lundin, S.B. (2017). CD4+ regulatory T cells in gastric cancer mucosa are proliferating and express high levels of IL-10 but little TGF-β. Gastric Cancer 20: 116–125. https://doi.org/10.1007/s10120-015-0591-z.Search in Google Scholar PubMed

Klebe, G. (2006). Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11: 580–594. https://doi.org/10.1016/j.drudis.2006.05.012.Search in Google Scholar PubMed PubMed Central

Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al.. (2010). DrugBank 3.0: a comprehensive resource for ’omics’ research on drugs. Nucl. Acids Res. 39: D1035–D1041.https://doi.org/10.1093/nar/gkq1126.Search in Google Scholar PubMed PubMed Central

Kurcinski, M., Jamroz, M., Blaszczyk, M., Kolinski, A., and Kmiecik, S. (2015). CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucl. Acids Res. 43: W419–W424. https://doi.org/10.1093/nar/gkv456.Search in Google Scholar PubMed PubMed Central

Scognamiglio, P.L., Di Natale, C., Perretta, G., and Marasco, D. (2013). From peptides to small molecules: an intriguing but intricated way to new drugs. Curr. Med. Chem. 20: 3803–3817. https://doi.org/10.2174/09298673113209990184.Search in Google Scholar PubMed

Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al.. (2014). DrugBank 4.0: shedding new light on drug metabolism. Nucl. Acids Res. 42: D1091–D1097. https://doi.org/10.1093/nar/gkt1068.Search in Google Scholar PubMed PubMed Central

Lee, G.R. (2017). Phenotypic and functional properties of tumor-infiltrating regulatory T cells. Mediat. Inflamm. 2017: 1–9, doi:https://doi.org/10.1155/2017/5458178.Search in Google Scholar PubMed PubMed Central

Leffers, N., Gooden, M.J., de Jong, R.A., Hoogeboom, B.N., Klaske, A., Hollema, H., Boezen, H.M., van der Zee, A.G., Daemen, T., and Nijman, H.W. (2009). Prognostic significance of tumor-infiltrating T-lymphocytes in primary and metastatic lesions of advanced stage ovarian cancer. Canc. Immunol. Immunother. 58: 449. https://doi.org/10.1007/s00262-008-0583-5.Search in Google Scholar PubMed

Li, W. and Godzik, A. (2006). Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658–1659. https://doi.org/10.1093/bioinformatics/btl158.Search in Google Scholar PubMed

Liang, Y.J., Liu, H.C., Su, Y.X., Zhang, T.H., Chu, M., Liang, L.Z., and Liao, G.Q. (2011). Foxp3 expressed by tongue squamous cell carcinoma cells correlates with clinicopathologic features and overall survival in tongue squamous cell carcinoma patients. Oral Oncol. 47: 566–570. https://doi.org/10.1016/j.oraloncology.2011.04.017.Search in Google Scholar PubMed

Lin, Y.C., Mahalingam, J., Chiang, J.M., Su, P.J., Chu, Y.Y., Lai, H.Y., Fang, J.H., Huang, C.T., Chiu, C.T., and Lin, C.Y. (2013). Activated but not resting regulatory T cells accumulated in tumor microenvironment and correlated with tumor progression in patients with colorectal cancer. Int. J. Canc. 132: 1341–1350. https://doi.org/10.1002/ijc.27784.Search in Google Scholar PubMed

Litfin, T., Zhou, Y., and Yang, Y. (2017). SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library. Bioinformatics 33: 1238–1240. https://doi.org/10.1093/bioinformatics/btw829.Search in Google Scholar PubMed

Naito, Y., Yamada, T., Ui-Tei, K., Morishita, S., and Saigo, K. (2004). siDirect: highly effective, target-specific siRNA design software for mammalian RNA interference. Nucleic Acids Res. 32: W124–W129. https://doi.org/10.1093/nar/gkh442.Search in Google Scholar PubMed PubMed Central

Naito, Y., Yoshimura, J., Morishita, S., and Ui-Tei, K. (2009). siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect. BMC Bioinf. 10: 1–8. https://doi.org/10.1186/1471-2105-10-392.Search in Google Scholar PubMed PubMed Central

Onizuka, S., Tawara, I., Shimizu, J., Sakaguchi, S., Fujita, T., and Nakayama, E. (1999). Tumor rejection by in vivo administration of anti-CD25 (interleukin-2 receptor?) monoclonal antibody. Canc. Res. 59: 3128–3133.Search in Google Scholar

Pellecchia, M. (2013). Antagonists of protein-protein interactions made easy? J. Med. Chem. 56: 13–14. https://doi.org/10.1021/jm301837n.Search in Google Scholar PubMed

Rickert, M., Wang, X., Boulanger, M.J., Goriatcheva, N., and Garcia, K.C. (2005). The structure of interleukin-2 complexed with its alpha receptor. Science 308: 1477–1480. https://doi.org/10.1126/science.1109745.Search in Google Scholar PubMed

Robb, R.J., Rusk, C.M., and Neeper, M.P. (1988). Structure-function relationships for the interleukin 2 receptor: location of ligand and antibody binding sites on the Tac receptor chain by mutational analysis. Proc. Natl. Acad. Sci. U.S.A. 85: 5654–5658. https://doi.org/10.1073/pnas.85.15.5654.Search in Google Scholar PubMed PubMed Central

Sauve, K., Nachman, M., Spence, C., Bailon, P., Campbell, E., Tsien, W.H., Kondas, J.A., Hakimi, J., and Ju, G. (1991). Localization in human interleukin 2 of the binding site to the alpha chain (p55) of the interleukin 2 receptor. Proc. Natl. Acad. Sci. U.S.A. 88: 4636–4640. https://doi.org/10.1073/pnas.88.11.4636.Search in Google Scholar PubMed PubMed Central

Sayour, E.J., McLendon, P., McLendon, R., De Leon, G., Reynolds, R., Kresak, J., Sampson, J.H., and Mitchell, D.A. (2015). Increased proportion of FoxP3+ regulatory T cells in tumor infiltrating lymphocytes is associated with tumor recurrence and reduced survival in patients with glioblastoma. Canc. Immunol. Immunother. 64: 419–427. https://doi.org/10.1007/s00262-014-1651-7.Search in Google Scholar PubMed PubMed Central

Tao, H., Mimura, Y., Aoe, K., Kobayashi, S., Yamamoto, H., Matsuda, E., Okabe, K., Matsumoto, T., Sugi, K., and Ueoka, H. (2012). Prognostic potential of FOXP3 expression in non-small cell lung cancer cells combined with tumor-infiltrating regulatory T cells. Lung Canc. 75: 95–101. https://doi.org/10.1016/j.lungcan.2011.06.002.Search in Google Scholar PubMed

Schindler, C.E., de Vries, S.J., and Zacharias, M. (2015). Fully blind peptide-protein docking with pepATTRACT. Structure 23: 1507–1515. https://doi.org/10.1016/j.str.2015.05.021.Search in Google Scholar PubMed

Schmidtke, P., Le Guilloux, V., Maupetit, J., and Tuffery, P. (2010). Fpocket: online tools for protein ensemble pocket detection and tracking. Nucl. Acids Res. 38: W582–W589. https://doi.org/10.1093/nar/gkq383.Search in Google Scholar PubMed PubMed Central

Shah, W., Yan, X., Jing, L., Zhou, Y., Chen, H., and Wang, Y. (2011). A reversed CD4/CD8 ratio of tumor-infiltrating lymphocytes and a high percentage of CD4+ FOXP3+ regulatory T cells are significantly associated with clinical outcome in squamous cell carcinoma of the cervix. Cell. Mol. Immunol. 8: 59–66. https://doi.org/10.1038/cmi.2010.56.Search in Google Scholar PubMed PubMed Central

Shang, B., Liu, Y., Jiang, S.J., and Liu, Y. (2015). Prognostic value of tumor-infiltrating FoxP3+ regulatory T cells in cancers: a systematic review and meta-analysis. Sci. Rep. 5: 15179. https://doi.org/10.1038/srep15179.Search in Google Scholar PubMed PubMed Central

Shimizu, J., Yamazaki, S., and Sakaguchi, S. (1999). Induction of tumor immunity by removing CD25+ CD4+ T cells: a common basis between tumor immunity and autoimmunity. J. Immunol. 163: 5211–5218.10.4049/jimmunol.163.10.5211Search in Google Scholar

Shirley, M. (2017). Daclizumab: a review in relapsing multiple sclerosis. Drugs 77: 447–458. https://doi.org/10.1007/s40265-017-0708-2.Search in Google Scholar PubMed

Shoichet, B.K. (2004). Virtual screening of chemical libraries. Nature 432: 862–865. https://doi.org/10.1038/nature03197.Search in Google Scholar PubMed PubMed Central

Strauss, L., Bergmann, C., Gooding, W., Johnson, J.T., and Whiteside, T.L. (2007). The frequency and suppressor function of CD4+ CD25highFoxp3+ T cells in the circulation of patients with squamous cell carcinoma of the head and neck. Clin. Canc. Res. 13: 6301–6311. https://doi.org/10.1158/1078-0432.ccr-07-1403.Search in Google Scholar PubMed

Tilley, J.W., Chen, L., Fry, D.C., Emerson, S.D., Powers, G.D., Biondi, D., Varnell, T., Trilles, R., Guthrie, R., Mennona, F., et al.. (1997). Identification of a small molecule inhibitor of the IL-2/IL-2R? receptor interaction which binds to IL-2. J. Am. Chem. Soc. 119: 7589–7590. https://doi.org/10.1021/ja970702x.Search in Google Scholar

Ui-Tei, K., Naito, Y., Nishi, K., Juni, A., and Saigo, K. (2008). Thermodynamic stability and Watson-Crick base pairing in the seed duplex are major determinants of the efficiency of the siRNA-based off-target effect. Nucl. Acids Res. 36: 7100–7109. https://doi.org/10.1093/nar/gkn902.Search in Google Scholar PubMed PubMed Central

Vagner, J., Qu, H., and Hruby, V.J. (2008). Peptidomimetics, a synthetic tool of drug discovery. Curr. Opin. Chem. Biol. 12: 292–296. https://doi.org/10.1016/j.cbpa.2008.03.009.Search in Google Scholar PubMed PubMed Central

Volkamer, A., Kuhn, D., Grombacher, T., Rippmann, F., and Rarey, M. (2012). Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 52: 360–372. https://doi.org/10.1021/ci200454v.Search in Google Scholar PubMed

Volkamer, A., Kuhn, D., Rippmann, F., and Rarey, M. (2012). DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28: 2074–2075. https://doi.org/10.1093/bioinformatics/bts310.Search in Google Scholar PubMed

Weiss, L., Melchardt, T., Egle, A., Grabmer, C., Greil, R., and Tinhofer, I. (2011). Regulatory T cells predict the time to initial treatment in early stage chronic lymphocytic leukemia. Cancer 117: 2163–2169. https://doi.org/10.1002/cncr.25752.Search in Google Scholar PubMed

Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., Marcu, A., Grant, J.R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al.. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucl. Acids Res. 46: D1074–D1082. https://doi.org/10.1093/nar/gkx1037.Search in Google Scholar PubMed PubMed Central

Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., and Hassanali, M. (2008). DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucl. Acids Res. 36: D901–D906. https://doi.org/10.1093/nar/gkm958.Search in Google Scholar PubMed PubMed Central

Kawabata, T. (2010). Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins 78: 1195–1211. https://doi.org/10.1002/prot.22639.Search in Google Scholar PubMed

Wu, Z., Johnson, K.W., Goldstein, B., Choi, Y., Eaton, S.F., Laue, T.M., and Ciardelli, T.L. (1995). Solution assembly of a soluble, heteromeric, high affinity interleukin-2 receptor complex. J. Biol. Chem. 270: 16039–16044. https://doi.org/10.1074/jbc.270.27.16039.Search in Google Scholar PubMed

Yang, Y., Zhan, J., and Zhou, Y. (2016). SPOT-Ligand: fast and effective structure-based virtual screening by binding homology search according to ligand and receptor similarity. J. Comput. Chem. 37: 1734–1739. https://doi.org/10.1002/jcc.24380.Search in Google Scholar PubMed

Yang, Z.Z., Novak, A.J., Stenson, M.J., Witzig, T.E., and Ansell, S.M. (2006). Intratumoral CD4+ CD25+ regulatory T-cell-mediated suppression of infiltrating CD4+ T cells in B-cell non-Hodgkin lymphoma. Blood 107: 3639–3646. https://doi.org/10.1182/blood-2005-08-3376.Search in Google Scholar PubMed PubMed Central

Yuan, X.L., Chen, L., Li, M.X., Dong, P., Xue, J., Wang, J., Zhang, T.T., Wang, X.A., Zhang, F.M., Ge, H.L., et al.. (2010). Elevated expression of Foxp3 in tumor-infiltrating Treg cells suppresses T-cell proliferation and contributes to gastric cancer progression in a COX-2-dependent manner. Clin. Immunol. 134: 277–288. https://doi.org/10.1016/j.clim.2009.10.005.Search in Google Scholar PubMed

Zhou, Q., Munger, M.E., Highfill, S.L., Tolar, J., Weigel, B.J., Riddle, M., Sharpe, A.H., Vallera, D.A., Azuma, M., Levine, B.L., et al.. (2010). Program death-1 signaling and regulatory T cells collaborate to resist the function of adoptively transferred cytotoxic T lymphocytes in advanced acute myeloid leukemia. Blood 116: 2484–2493. https://doi.org/10.1182/blood-2010-03-275446.Search in Google Scholar PubMed PubMed Central

Received: 2020-10-02
Accepted: 2020-10-22
Published Online: 2020-11-19
Published in Print: 2021-01-27

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